Ponce, Hiram
Preferred name
Ponce, Hiram
Official Name
Ponce Espinosa, Hiram Eredín
Alternative Name
hponce
Main Affiliation
ORCID
0000-0002-6559-7501
Scopus Author ID
54911890000
Researcher ID
K-7593-2019
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Item type:Publication, Modular IoT Hydroponics System(MDPI AG, 2025-10-31) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Novel Ethical Design Framework Applied to Image Classification Challenges in the Fashion Industry(Springer Nature Switzerland, 2025); As artificial intelligence (AI) continues to play a pivotal role in image classification applications, the ethical implications of these technologies become increasingly significant. This paper explores the intersection of AI and ethics in the context of image classification, specifically focusing on the application of ethical design principles through a framework for a use of case in the fashion industry involving bags images and social media. This work delves into the integration of a comprehensive ethical framework around all the design process. The case study involves the development and implementation of a neural network tailored for bag image classification, leveraging transfer learning techniques. Through a meticulous examination of the ethical dimensions inherent in image classification, the study aims to establish a foundation for responsible and transparent AI practices. ©The authors ©Springer. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Automatic Robotics Medication Delivery System: The ANDIS Case Study(Springer Nature Switzerland, 2025-10-11) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Causal Artificial Intelligence in Legal Language Processing: A Systematic Review(MDPI, 2025); Recent advances in legal language processing have highlighted limitations in correlation-based artificial intelligence approaches, prompting exploration of Causal Artificial Intelligence (AI) techniques for improved legal reasoning. This systematic review examines the challenges, limitations, and potential impact of Causal AI in legal language processing compared to traditional correlation-based methods. Following the Joanna Briggs Institute methodology, we analyzed 47 papers from 2017 to 2024 across academic databases, private sector publications, and policy documents, evaluating their contributions through a rigorous scoring framework assessing Causal AI implementation, legal relevance, interpretation capabilities, and methodological quality. Our findings reveal that while Causal AI frameworks demonstrate superior capability in capturing legal reasoning compared to correlation-based methods, significant challenges remain in handling legal uncertainty, computational scalability, and potential algorithmic bias. The scarcity of comprehensive real-world implementations and overemphasis on transformer architectures without causal reasoning capabilities represent critical gaps in current research. Future development requires balanced integration of AI innovation with law’s narrative functions, particularly focusing on scalable architectures for maintaining causal coherence while preserving interpretability in legal analysis. ©The authors ©Entropy ©MDPI. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Towards a Distributed-Based Learning Robot from Scratch via Neuro-Evolutionary Computation(Springer Nature Switzerland, 2025-10-20); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine Learning Methods in Biomedical Field : Computer-Aided Diagnostics, Healthcare and Biology Applications(Springer Nature Switzerland, 2026); ; ; - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, A Conceptual Design of a Firefighter Drone(2018)Scopus© Citations 17 26 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A non-contact SpO2 estimation using a video magnification technique(2021); ; In this paper, we present a new non-contact strategy to estimate the Peripheral Oxygen Saturation (SpO2) based on the Eulerian motion video magnification technique and a signal processing technique. The magnification procedure was carried out using two approaches: the Hermite decomposition and the Gaussian decomposition. The SpO2 is estimated from the signals extracted after magnification process using the red and the blue channel of the image frame. We have tested the method on five healthy subjects using videos obtained from the googlemeet video conference platform. Each video includes the subject and the data of the contact pulse oximeter device. To compare the performance of the methods, we compute the mean average error and metrics issues from the Bland and Altman analysis to investigate the agreement of the methods with respect to a contact pulse oximeter device as reference. The proposed solution shows an agreement with respect to the reference of most of 98%. These preliminary results are promising for the implementation in a remote medical consultation setting. © 2021 SPIE.Scopus© Citations 3 23 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Price Estimation for Pre-owned Vehicles Using Machine Learning(2024)14